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arXiv · 1904.06276

Blind search for complex chemical pathways using Harmonic Linear Discriminant Analysis

Abstract

Disentangling the mechanistic details of a chemical reaction pathway is a hard problem that often requires a considerable amount of chemical intuition and a component of luck. Experiments struggle in observing short-life metastable intermediates, while computer simulations often rely upon a good initial guess. In this work, we propose a method that, from the simulations of a reactant and a product state, searches for reaction mechanisms connecting the two by exploring the configuration space through metadynamics, a well known enhanced molecular dynamics method. The key quantity underlying this search is based on the use of an approach called Harmonic Linear Discriminant Analysis which allows a systematic construction of collective variables. Given the reactant and product states, we choose a set of descriptors capable of discriminating between the two states. In order not to prejudge the results, generic descriptors are introduced. The fluctuations of the descriptors in the two states are used to construct collective variables. We use metadynamics in an exploratory mode to discover the intermediates and the transition states that lead from reactant to product. The search is at first conducted at a low theory level. The calculation is then refined and the energy of the intermediates and transition states discovered during metadynamics is computed again using a higher level of theory. The method's aim is to offer a simple reaction mechanism search procedure that helps in saving time and is able to find unexpected mechanisms that defy well established chemical paradigms. We apply it to two reactions, showing that a high level of complexity can be hidden even in seemingly trivial and small systems. The method can be applied to larger systems, such as reactions in solution or catalysis.

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Valerio Rizzi, Dan Mendels, Emilia Sicilia, Michele Parrinello. 2019-07-31. Blind search for complex chemical pathways using Harmonic Linear Discriminant Analysis. https://doi.org/10.1021/acs.jctc.9b00358

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